ArticleJournal of computational neuroscience2018
Synaptic efficacy shapes resource limitations in working memory.
Article in Journal of computational neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Neural circuit models for evidence accumulation through choice-selective sequences.Nature communications · 2026Article
- Robustly encoding certainty in a metastable neural circuit model.Physical review. E · 2024Article
- Cholinergic Neuromodulation of Prefrontal Attractor Dynamics Controls Performance in Spatial Working Memory.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Cholinergic neuromodulation of prefrontal attractor dynamics controls performance in spatial working memory.bioRxiv : the preprint server for biology · 2024Article
- MULTISCALE MOTION AND DEFORMATION OF BUMPS IN STOCHASTIC NEURAL FIELDS WITH DYNAMIC CONNECTIVITY.Multiscale modeling & simulation : a SIAM interdisciplinary journal · 2024Article
- Multiple bumps can enhance robustness to noise in continuous attractor networks.PLoS computational biology · 2022Article
- Strategy-dependent effects of working-memory limitations on human perceptual decision-making.eLife · 2022Article
- Distinct Excitatory and Inhibitory Bump Wandering in a Stochastic Neural Field.SIAM journal on applied dynamical systems · 2022Article
- A dynamic neural field model of continuous input integration.Biological cybernetics · 2021Article
- Across-Area Synchronization Supports Feature Integration in a Biophysical Network Model of Working Memory.Frontiers in neural circuits · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Working memory (WM) is limited in its temporal length and capacity. Classic conceptions of WM capacity assume the system possesses a finite number of slots, but recent evidence suggests WM may be a continuous resource. Resource models typically assume there is no hard upper bound on the number of items that can be stored, but WM fidelity decreases with the number of items. We analyze a neural field model of multi-item WM that associates each item with the location of a bump in a finite spatial domain, considering items that span a one-dimensional continuous feature space. Our analysis relates the neural architecture of the network to accumulated errors and capacity limitations arising during the delay period of a multi-item WM task. Networks with stronger synapses support wider bumps that interact more, whereas networks with weaker synapses support narrower bumps that are more susceptible to noise perturbations. There is an optimal synaptic strength that both limits bump interaction events and the effects of noise perturbations. This optimum shifts to weaker synapses as the number of items stored in the network is increased. Our model not only provides a circuit-based explanation for WM capacity, but also speaks to how capacity relates to the arrangement of stored items in a feature space.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.